One computer implemented approach for computing demand forecast information for a demand forecast application involves defining a so-called demand forecast tree capable of being graphically represented by a single top level node (00) with at least two branches directly emanating therefrom, each branch having at least one node, for example, bottom level node (11) (see FIG. 1). The demand forecast information is computed on the basis of time series of observations typically associated with bottom level nodes by a forecast engine capable of determining a mathematical simulation model for a demand process. One such forecast engine employing statistical seasonal causal time series models of count data is commercially available from Demantra Ltd, Israel, under the name Demantra™ Demand Planner.
One exemplary demand forecast application is the media distribution problem, namely, determining the number of copies of a daily newspaper to be delivered daily to an outlet to minimize two mutually conflicting indices commonly quantified for evaluating the efficacy of a distribution policy for a newspaper over an evaluation period: the frequency of sellouts, and the number of return typically expressed in percentage terms of total returns over total draw. In this connection, it is a common practice in the industry that a draw for a newspaper at an outlet for a given day is greater than its demand forecast at that outlet for that day so to reduce the probability of a sellout but with the inherent downside that the probability of returns is greater. In the case of distribution systems for newspapers, the safety stock is typically intended to provide a level of safety of around 80±10% for a given probability function for the demand for the newspaper at the outlet.
The media distribution problem is a particular realization of the well-known single period stochastic inventory problem which has been the subject of considerable academic interest. It has been long recognized that occurrences of sellouts downwardly bias demand forecasts due to actual sales data reflecting stock availability levels as opposed to true demand. In view of this, researchers in the area of demand forecasting have developed procedures to cater for presence of sellouts by computing demand forecasts on adjusted sales data. One exemplar approach is set out in an article entitled “Forecasting demand variation when there are stockouts”, Bell, P. C., Journal of the Operational Research Society (2000) 51, 358-363.